The paper develops a model for combining point forecasts into a predictive distribution for a variable of interest. Our approach allows for point forecasts to be correlated and admits uncertainty on the distribution parameters given the forecasts. Further, it provides an easy way to compute an augmentation factor needed to equate the dispersion of the point forecasts to that of the predictive distribution, which depends on the correlation between the point forecasts and on the number of forecasts. We show that ignoring dependence or parameter uncertainty can lead to assuming an unrealistically narrow predictive distribution. We further illustrate the implications in a newsvendor context, where our model leads to an order quantity that has higher variance but is biased in the less costly direction, and generates an increase in expected profit relative to other methods. The e-companion is available at https://doi.org/10.1287/mnsc.2017.2936. This paper was accepted by Vishal Gaur, operations management.
{"title":"Assessing Uncertainty from Point Forecasts","authors":"Anil Gaba, Dana G. Popescu, Zhi Chen","doi":"10.2139/ssrn.2807764","DOIUrl":"https://doi.org/10.2139/ssrn.2807764","url":null,"abstract":"The paper develops a model for combining point forecasts into a predictive distribution for a variable of interest. Our approach allows for point forecasts to be correlated and admits uncertainty on the distribution parameters given the forecasts. Further, it provides an easy way to compute an augmentation factor needed to equate the dispersion of the point forecasts to that of the predictive distribution, which depends on the correlation between the point forecasts and on the number of forecasts. We show that ignoring dependence or parameter uncertainty can lead to assuming an unrealistically narrow predictive distribution. We further illustrate the implications in a newsvendor context, where our model leads to an order quantity that has higher variance but is biased in the less costly direction, and generates an increase in expected profit relative to other methods. The e-companion is available at https://doi.org/10.1287/mnsc.2017.2936. This paper was accepted by Vishal Gaur, operations management.","PeriodicalId":365198,"journal":{"name":"INSEAD: Decision Science (Topic)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116535084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This report focuses on the question of value creation in private equity and presents a framework to clearly identify the sources of return and operational value creation by the GPs in PE investment.The framework - IVC 2.0 - deconstructs value creation across various financial and operational drivers and isolates value created through capital structure re-engineering, industry operating performance, and company-specific Alpha.
{"title":"Value Creation 2.0 – A Framework for Measuring Value Creation in Private Equity","authors":"Claudia Zeisberger, Bowen White, Michael Prahl","doi":"10.2139/ssrn.2766896","DOIUrl":"https://doi.org/10.2139/ssrn.2766896","url":null,"abstract":"This report focuses on the question of value creation in private equity and presents a framework to clearly identify the sources of return and operational value creation by the GPs in PE investment.The framework - IVC 2.0 - deconstructs value creation across various financial and operational drivers and isolates value created through capital structure re-engineering, industry operating performance, and company-specific Alpha.","PeriodicalId":365198,"journal":{"name":"INSEAD: Decision Science (Topic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132553597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-05-30DOI: 10.1017/CBO9781107587793.009
Natalia Karelaia, J. Reb
Decisions shape personal and organizational outcomes, and both researchers and practitioners look for ways to enhance decision making prowess. In this chapter, we explore whether and how mindfulness might help at various stages of decision making. We suggest that mindfulness may help notice when a decision should or could be made, increase goal awareness, enhance consistency of the decision with one’s fundamental values, facilitate option generation, reduce the sunk cost bias, and help recognize ethical challenges of decisions. We further argue that while mindfulness may diminish the scope of information search, it may also improve the quality of information used to make a decision. It may also reduce confirmation bias and overconfidence, allow decision makers to better differentiate between relevant and irrelevant information, reduce reliance on stereotypes, help appreciate uncertainty and productively deal with it, and reduce illusory pattern detection. Furthermore, mindfulness is likely to facilitate resolving trade-offs and help effectively reconcile intuition with analysis thereby reducing procrastination. Finally, mindful decision makers are more likely to learn to make better decisions over time because they are more open to feedback and less prone to misinterpret it by making self-serving attributions. The potential of mindfulness to improve judgment and decisions provides many promising opportunities for future research.
{"title":"Improving Decision Making Through Mindfulness","authors":"Natalia Karelaia, J. Reb","doi":"10.1017/CBO9781107587793.009","DOIUrl":"https://doi.org/10.1017/CBO9781107587793.009","url":null,"abstract":"Decisions shape personal and organizational outcomes, and both researchers and practitioners look for ways to enhance decision making prowess. In this chapter, we explore whether and how mindfulness might help at various stages of decision making. We suggest that mindfulness may help notice when a decision should or could be made, increase goal awareness, enhance consistency of the decision with one’s fundamental values, facilitate option generation, reduce the sunk cost bias, and help recognize ethical challenges of decisions. We further argue that while mindfulness may diminish the scope of information search, it may also improve the quality of information used to make a decision. It may also reduce confirmation bias and overconfidence, allow decision makers to better differentiate between relevant and irrelevant information, reduce reliance on stereotypes, help appreciate uncertainty and productively deal with it, and reduce illusory pattern detection. Furthermore, mindfulness is likely to facilitate resolving trade-offs and help effectively reconcile intuition with analysis thereby reducing procrastination. Finally, mindful decision makers are more likely to learn to make better decisions over time because they are more open to feedback and less prone to misinterpret it by making self-serving attributions. The potential of mindfulness to improve judgment and decisions provides many promising opportunities for future research.","PeriodicalId":365198,"journal":{"name":"INSEAD: Decision Science (Topic)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128527992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper compares the position of strategic buyers and private equity buyers in a typical investment process, from fundraising/sourcing of equity capital, through deal sourcing, due diligence, valuation and deal financing, deal negotiation and transaction execution, to value creation post-closing and exit/long-term plans. The paper evaluates advantages and disadvantages of the two different types of buyers within each of the phases of an investment process, and suggests that the financial discipline, flexibility, focus, and incentives structure of private equity firms often provide them with an edge over strategic buyers. Rather than being a rigorous academic study, the paper is intended to help M&A practitioners, whether on the strategic buyer or private equity side, with understanding their “opponent”.
{"title":"Strategic Buyers vs. Private Equity Buyers in an Investment Process","authors":"Jan Vild, Claudia Zeisberger","doi":"10.2139/ssrn.2439589","DOIUrl":"https://doi.org/10.2139/ssrn.2439589","url":null,"abstract":"This paper compares the position of strategic buyers and private equity buyers in a typical investment process, from fundraising/sourcing of equity capital, through deal sourcing, due diligence, valuation and deal financing, deal negotiation and transaction execution, to value creation post-closing and exit/long-term plans. The paper evaluates advantages and disadvantages of the two different types of buyers within each of the phases of an investment process, and suggests that the financial discipline, flexibility, focus, and incentives structure of private equity firms often provide them with an edge over strategic buyers. Rather than being a rigorous academic study, the paper is intended to help M&A practitioners, whether on the strategic buyer or private equity side, with understanding their “opponent”.","PeriodicalId":365198,"journal":{"name":"INSEAD: Decision Science (Topic)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115409637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ilia Tsetlin, R. L. Winkler, Rachel J. Huang, Larry Y. Tzeng
Almost stochastic dominance allows small violations of stochastic dominance rules to avoid situations where most decision makers prefer one alternative to another but stochastic dominance cannot rank them. While the idea behind almost stochastic dominance is quite promising, it has not caught on in practice. Implementation issues and inconsistencies between integral conditions and their associated utility classes contribute to this situation. We develop generalized almost second-degree stochastic dominance and almost second-degree risk in terms of the appropriate utility classes and their corresponding integral conditions, and extend these concepts to higher degrees. We address implementation issues and show that generalized almost stochastic dominance inherits the appealing properties of stochastic dominance. Finally, we define convex generalized almost stochastic dominance to deal with risk-prone preferences. Generalized almost stochastic dominance could be useful in decision analysis, empirical research (e.g., in finance), and theoretical analyses of applied situations.
{"title":"Generalized Almost Stochastic Dominance","authors":"Ilia Tsetlin, R. L. Winkler, Rachel J. Huang, Larry Y. Tzeng","doi":"10.2139/ssrn.2327022","DOIUrl":"https://doi.org/10.2139/ssrn.2327022","url":null,"abstract":"Almost stochastic dominance allows small violations of stochastic dominance rules to avoid situations where most decision makers prefer one alternative to another but stochastic dominance cannot rank them. While the idea behind almost stochastic dominance is quite promising, it has not caught on in practice. Implementation issues and inconsistencies between integral conditions and their associated utility classes contribute to this situation. We develop generalized almost second-degree stochastic dominance and almost second-degree risk in terms of the appropriate utility classes and their corresponding integral conditions, and extend these concepts to higher degrees. We address implementation issues and show that generalized almost stochastic dominance inherits the appealing properties of stochastic dominance. Finally, we define convex generalized almost stochastic dominance to deal with risk-prone preferences. Generalized almost stochastic dominance could be useful in decision analysis, empirical research (e.g., in finance), and theoretical analyses of applied situations.","PeriodicalId":365198,"journal":{"name":"INSEAD: Decision Science (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131088916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the dynamic pricing implications of a new, behaviorally motivated reference price mechanism based on the peak-end memory mode. This model suggests that consumers anchor on a reference price that is a weighted average of the lowest and most recent prices. Loss-averse consumers are more sensitive to perceived losses than gains relative to this reference price. We find that a range of constant pricing policies is optimal for the corresponding dynamic pricing problem. This range is wider the more consumers anchor on lowest prices, and it persists when buyers are loss neutral, in contrast with previous literature. In a transient regime, the optimal pricing policy is monotone and converges to a steady-state price, which is lower the more extreme and salient the low-price anchor is. Our results suggest that behavioral regularities, such as peak-end anchoring and loss aversion, limit the benefits of varying prices, and caution that the adverse effects of deep discounts on the firm's optimal prices and profits might be more enduring than previous models predict.
{"title":"Dynamic Pricing with Loss Averse Consumers and Peak-End Anchoring","authors":"Javad Nasiry, I. Popescu","doi":"10.1287/opre.1110.0952","DOIUrl":"https://doi.org/10.1287/opre.1110.0952","url":null,"abstract":"We study the dynamic pricing implications of a new, behaviorally motivated reference price mechanism based on the peak-end memory mode. This model suggests that consumers anchor on a reference price that is a weighted average of the lowest and most recent prices. Loss-averse consumers are more sensitive to perceived losses than gains relative to this reference price. We find that a range of constant pricing policies is optimal for the corresponding dynamic pricing problem. This range is wider the more consumers anchor on lowest prices, and it persists when buyers are loss neutral, in contrast with previous literature. In a transient regime, the optimal pricing policy is monotone and converges to a steady-state price, which is lower the more extreme and salient the low-price anchor is. Our results suggest that behavioral regularities, such as peak-end anchoring and loss aversion, limit the benefits of varying prices, and caution that the adverse effects of deep discounts on the firm's optimal prices and profits might be more enduring than previous models predict.","PeriodicalId":365198,"journal":{"name":"INSEAD: Decision Science (Topic)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128854987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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